An efficient simulation model for rack design in multi-elevator shuttle-based storage and retrieval system
Introduction
A shuttle-based storage and retrieval system (SBS/RS) is a relatively new material-handling facility, composed of shuttle carriers (vehicle), elevators with a lifting table (lift), and storage racks. The SBS/RS is the subset of an autonomous vehicle storage and retrieval system (AVS/RS). The main difference between an AVS/RS and a traditional automated storage and retrieval system (AS/RS) is the movement patterns of the storage and retrieval (S/R) devices [1]. In an AS/RS, the unit loads are handled by cranes that simultaneously move in horizontal and vertical directions, whereas in an AVS/RS, the unit loads are handled by autonomous vehicles (shuttle carriers) moving horizontally and by an elevator (with lifting table) moving vertically. Therefore, a number of storage and retrieval commands can be performed simultaneously in an AVS/RS (SBS/RS) and better performance may be achieved.
Depending on the vehicle assignment to storage tiers, there are two main rack configurations within AVS/RS: ‘tier-to-tier’ and ‘tier-captive’ [2]. In this paper, a tier-captive configuration type SBS/RS is studied. Theoretically, an SBS/RS can deliver better throughput by using more shuttle carriers. However, the lifting tables are a bottleneck in the tier-captive configuration because the number of elevators is determined by the number of aisles. In order to improve performance, new tier-captive configurations equipped with multiple elevators per aisle have been developed in recent years. Multi shuttle Captive is one such example; it was developed by Dematic and has been used in Europe and China.
It is crucial to design an SBS/RS in such a way that it can efficiently handle different requirements, without any bottlenecks and excess capacity [3]. However, this multi-elevator technology has resulted in more rack alternatives compared to a traditional single elevator SBS/RS. This is because the multi-elevator system allows the introduction of new design variables, such as the number of elevators, the elevator position, and the buffer position. In order to find the appropriate rack configuration, designers have to simulate and evaluate a large number of alternatives. If there are too many alternatives, it is hard to find the best one due to time and money constraints.
In order to simplify the performance evaluation and to facilitate faster rack design, we propose an efficient simulation model, which can be used in the early technology selection or in the conceptualization phase of the system development. Compared to conventional abstract simulation models [3], [4], [14], the animation of the proposed model is identical to the real system, which can help shareholders to understand the design. More important, the proposed model can be auto-remodeled for different alternatives. Consequently, the tedious manual modeling work can be significantly reduced and a large number of alternatives can be efficiently evaluated in a tolerable period of time. The scientific contribution of our work is enabling wide range performance evaluation of multi-elevator SBS/RS scenarios via simulation.
This paper is organized as follows. Literature related to the performance evaluation of the SBS/RS is presented in Section 2. The rack design of the multi-elevator tier-captive SBS/RS is described in Section 3. The proposed simulation model is presented in Section 4. A simulation case study is presented in Section 5. Finally, conclusions are drawn in Section 6.
Section snippets
Literature review
Based on our knowledge, only a few studies are directly related to the SBS/RS, meanwhile a majority of studies pertain to the AVS/RS, which use the same technology as SBS/RS.
Malmborg first analyzed the performance of a tier-to-tier AVS/RS. A queuing model was proposed to estimate the cycle time performance considering the number of columns, tiers, vehicles, and lifts [5].
Following Malmborg's study, Kuo et al. used an M/G/V queue model nested within an M/G/L queue model to estimate the waiting
Problem description and analysis
The side and top views of the multi-elevator configuration under study are shown in Fig. 2. As can be seen in Fig. 2, on each tier of the facility, every elevator has a buffer-in and buffer-out position, which are used to temporarily store unit loads arriving from elevators/shuttle carriers to be transferred to shuttle carriers/elevators. The elevator is located in the middle; the buffer-in and buffer-out positions are located on either side of the elevator (see top view of Fig. 2). One shuttle
Simulation modeling and validation
As mentioned in the literature review, simulation is an effective method to study the performance of an SBS/RSs. Conventional SBS/RS simulation models [14] are not suitable for multi-elevator tier-captive SBS/RSs. In this section, we describe the building of a simulation model specifically for multi-elevator tier-captive SBS/RSs by using the eM-plant simulation package.
As mentioned in the literature review, to fully evaluate the alternative rack solutions, simulation needs to be accelerated. To
Simulation case study
We studied a real multi-elevator tier-captive SBS/RS by using the proposed simulation model. During the conceptualization phase of the system development, the arrival rate of orders is not definite. Besides, the maximal throughput and the minimal cycle time should be determined simultaneously. Therefore, the design of this system is a multi-objective optimization problem. Moreover, the final design solution should show excellent performance with different arrival rates.
Conclusion
In this study, we developed a model for simulating the rack configuration of a multi- elevator tier-captive SBS/RS. Our objective was to efficiently simulate different rack alternatives and gain a better understanding of the relationship between the rack configuration design and the system's performance.
As an example, a simulation case study was presented, which contains 81 rack alternatives, 15 retrieval rates and a total of 1215 simulation scenarios. The objective was to identify optimal rack
Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant no. 71301008. The authors would like to thank the anonymous reviewers and their constructive suggestions.
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